International journal of computer assisted radiology and surgery · 2009
An improved finite element model for craniofacial surgery simulation.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Wang Shengzheng, Yang Jie
Аннотация
PURPOSE: A novel approach is proposed for simulating the deformation of the facial soft tissues in the craniofacial surgery simulation.
METHODS: A nonlinear finite mixed-element model (NFM-EM) based on solid-shell elements and Lagrange principle of virtual work is proposed, which addresses the heterogeneity in geometry and material properties found in the soft tissues of the face. Moreover, after the investigation of the strain-potential models, the biomechanical characteristics of skin, muscles and fat are modeled with the most suitable material properties. In addition, an improved contact algorithm is used to compute the boundary conditions of the soft tissue model.
RESULTS: The quantitative validation and the comparative results with other models proved the effectiveness of the approach on the simulation of complex soft tissues. The average absolute value of errors stays below 0.5 mm and the 95% percentiles of the distance map is less than 1.5 mm.
CONCLUSIONS: NFM-EM promotes the accuracy and effectiveness of the soft tissue deformation, and the effective contact algorithm bridges the bone-related planning and the prediction of the target face.
METHODS: A nonlinear finite mixed-element model (NFM-EM) based on solid-shell elements and Lagrange principle of virtual work is proposed, which addresses the heterogeneity in geometry and material properties found in the soft tissues of the face. Moreover, after the investigation of the strain-potential models, the biomechanical characteristics of skin, muscles and fat are modeled with the most suitable material properties. In addition, an improved contact algorithm is used to compute the boundary conditions of the soft tissue model.
RESULTS: The quantitative validation and the comparative results with other models proved the effectiveness of the approach on the simulation of complex soft tissues. The average absolute value of errors stays below 0.5 mm and the 95% percentiles of the distance map is less than 1.5 mm.
CONCLUSIONS: NFM-EM promotes the accuracy and effectiveness of the soft tissue deformation, and the effective contact algorithm bridges the bone-related planning and the prediction of the target face.